Learning Bayesian Network Equivalence Classes with Ant Colony Optimization
This work addresses the challenge of efficiently learning Bayesian network structures for knowledge representation, but it appears incremental as it builds on existing ACO techniques with specific extensions.
The paper tackled the problem of learning Bayesian network structures by proposing ACO-E, a new algorithm that uses Ant Colony Optimization to search through equivalence classes, and it outperformed greedy search and other state-of-the-art methods in testing.
Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.